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A fuzzy energy-based active contour model with adaptive contrast constraint for local segmentation

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Abstract

Image segmentation is to divide an image into different parts or extract some interested objects. Active contour model and fuzzy clustering are two widely used segmentation methods, which have been integrated into an effective model in recent years. Local segmentation is often needful in medical image processing. In view of local segmentation on inhomogeneous images, a new average fuzzy energy-based active contour model is proposed in this paper, in which the total fuzzy energy integrates the approximate weighted average and arithmetic average variances of the image. And an adaptive contrast constraint condition is introduced to prevent the curve from falling into local minimum, which further improves the robustness of the segmentation model to initial contour. Experimental results on synthetic and medical images demonstrate that the proposed model has considerable improvements in terms of segmentation accuracy and robustness compared to several existing local segmentation models.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (81371635 and 81671848), Key Research and Development Project of Shandong Province (2016GGX101017) and Research Fund for the Doctoral Program of Higher Education of China (20120131110062).

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Authors and Affiliations

  1. School of Mechanical, Electrical and Information Engineering, Shandong University (Weihai), Weihai, 264209, China

    Wenyan Sun & Enqing Dong

  2. School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, 264005, China

    Wenyan Sun

  3. Department of Magnetic Resonance Imaging, Weihai Municipal Hospital, Weihai, 264200, China

    Huijie Qiao

Authors
  1. Wenyan Sun
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  2. Enqing Dong
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  3. Huijie Qiao
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Correspondence to Enqing Dong.

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Sun, W., Dong, E. & Qiao, H. A fuzzy energy-based active contour model with adaptive contrast constraint for local segmentation. SIViP 12, 91–98 (2018). https://doi.org/10.1007/s11760-017-1134-3

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  • DOI: https://doi.org/10.1007/s11760-017-1134-3

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